Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB

Background Recent innovations are making radiology more advanced for patient and patient services. Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB). Methods This prospective study included 143 patients in each group who were referred for computed tomography (CT) KUB examination. Group 1 patients underwent manual positioning (MP), and group 2 patients underwent AI-based automatic positioning (AP) for CT KUB examination. The scanning protocol was kept constant for both the groups. The off-center distance, RD, and quantitative and qualitative IQ of each group were evaluated and compared. Results The AP group (9.66±6.361 mm) had significantly less patient off-center distance than the MP group (15.12±9.55 mm). There was a significant reduction in RD in the AP group compared with that in the MP group. The quantitative image noise (IN) was lower, with a higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the AP group than in the MP group (p<0.05). Qualitative IQ parameters such as IN, sharpness, and overall IQ also showed significant differences (p< 0.05), with higher scores in the AP group than in the MP group. Conclusions The AI-based AP showed higher positioning accuracy with less off-center distance (44%), which resulted in 12% reduction in RD and improved IQ for CT KUB imaging compared with MP.


Introduction
Computed Tomography (CT) is a valuable imaging modality for the diagnosis of various pathologies.However, CT scans use X-rays, which involve exposure to ionizing radiation.5][6][7][8][9] In addition, proper patient positioning is crucial for obtaining higher image quality with an optimized radiation dose. 10diology medical technologists can utilize laser lights to visually evaluate the patient's central placement in CT imaging; however, this approach is user-dependent, and patient miscentering is common and well-documented problem that can have detrimental consequences. 11If the patient is placed away from the gantry isocenter (i.e., the table is too up or down), the localizer image will be either enlarged or reduced in width.3][14] For manual positioning, the interaction between the radiographers and the patient poses a risk of cross-infection in patients with infectious diseases. 14cently, an artificial intelligence (AI)-based positioning camera, which works based on an AI algorithm that uses intelligence (a body contour detection algorithm) to detect a patient's body using a three-dimensional (3D) camera. 15arious companies have introduced AI-based contactless positions for patients.A 3D camera equipped with a visible light camera, an infrared light source, and a sensor was installed above the patient.It adjusts the height of the table and maintains the patient within the isocenter of the gantry.5][16][17][18] Hence, this study aimed to evaluate and compare the positioning accuracy, IQ, and RD of AI-based automatic and manual positioning for CT Kidney Ureter and Bladder (KUB) imaging.

Study design
This is a prospective study.Ethical approval was obtained from the Institutional ethical committee (IEC 168/2023) of Kasturba Medical College and Hospital, Manipal, India on 7 th June 2023, and then the study was registered on Clinical Trial Registry-India (CTRI/2023/06/054173) on 20 th June 2023.Written informed consent was obtained from all participants for publication and participation in the data collection for the study.

Eligibility criteria
The study included a total of 286 patients, with 143 patients in each group referred for CT KUB Imaging for various clinical indications such as evaluation of renal calculi, flank pain, kidney masses, and traumatic injury to the kidneys.Patients who were uncooperative and those with CT KUB images with artifacts (movement and metal) were excluded.Patient age and BMI were noted, and only patients with normal BMI were included.All patients underwent CT using a Philips Incisive 128 Slice CT Scanner.

Patient positioning
Patients in group 1 Manual positioning (MP group) underwent CT KUB imaging using manual positioning.The patient was positioned supine on a scan table with the feet first towards the gantry, and the arms were extended and supported above the head.The table height was adjusted by the gantry-mounted adjustment button such that the horizontal laser beam coincided with the mid-coronal plane of the patient and to the gantry isocenter by visual inspection.The area covered the dome of the diaphragm immediately below the symphysis pubis.
Group 2 Automatic Positioning (AP group) patients underwent CT KUB imaging by AI-based automatic patient positioning, which included an AI-enabled camera mounted on the ceiling above the patient table.

REVISED Amendments from Version 1
In the Result section, the reviewer asked to correct the typographic errors in the unit of off-center distance as millimeter.The same is corrected in the Result section.
Any further responses from the reviewers can be found at the end of the article An AI-based camera automatically detected the patient's orientation in the supine position with the feet first into the gantry.After selecting the CT KUB protocol, the area of interest (from the diaphragm to the symphysis pubis) to be scanned was automatically detected using an AIbased camera, and the table height was adjusted to the gantry isocenter.

Image acquisition
The image acquisition parameters were kept the same for both groups such as use of ATCM, tube voltage of 120 kVp, rotation time of 0.75s, pitch 1.0, matrix 512*512, slice thickness and increment 3 mm.The MP group patient images was reconstructed with IR technique -iDose 4 -level 4 (Philips Health Care ®,TM ).The AP group patient images were reconstructed with DLIR technique (Precise Image; Philips Health Care ®,TM ).The axial CT images from both the groups were reconstructed to extended field of view (FOV) of 500 mm.

Off-center distance measurement
The off-center distance was measured to evaluate the accuracy of patient positioning.To calculate the off-center distance, an axial slice of the CT KUB image at the level of the fourth lumbar vertebra, with an Field of View (FOV) of 500 mm, was selected.A straight line was drawn that joins the anterior and posterior margins of the complete FOV, and the midpoint of this line was determined, which represents the gantry isocenter.Another straight line that joined the anterior and posterior surfaces of the patient was drawn, and the midpoint of this line was determined to represent the patient's center.The distance between the gantry isocenter and the patient's center was measured using a measuring tool to evaluate the off-center distance 12 (Figure 1).The scan length was noted in both the groups.

Radiation dose measurement
Radiation dose descriptors such as "Volumetric Computed Tomography Dose Index (CTDIv) in mGy," "Dose Length Product (DLP) in mGy.cm", "Size Specific Dose Estimate (SSDE) in mGy" was noted from the CT scanner and the "effective dose (ED)" was calculated using the following formula: E ¼ DLP X Conversion factorðKÞ (K= 0.015 mSv/ mGy.cm). 19antitative image quality Quantitative IQ was assessed by calculating "signal to noise ratio (SNR)," "contrast to noise ratio (CNR)" and "image noise (IN)." 3 mm slice thickness axial CT KUB images were selected, and six circular regions of interest (ROI) measuring 4-5 mm 2 in diameter were drawn in the following regions: upper poles of the kidneys, lower poles, subcutaneous fat, and psoas muscle (Figure 2A and 2B).

Results
The study included 286 patients referred for CT KUB imaging, 143 patients underwent CT KUB imaging using manual positioning, and the remaining 143 patients underwent automatic positioning.Patient details are summarized in Table 1.

Off-center distance
The mean off-center distance in the MP group and AP group was 15.12 AE 9.55 mm and 9.66 AE 6.361 mm.A statistically significant difference in the off-center distance (p < 0.05) was noted between the MP and AP group.The AP group showed 44% less off-center distance compared to the MP group.Scan length also showed a significant difference (p < 0.05) between the AP (56.0 AE 1.75 cm) and MP group (58.2 AE 3.55 cm).

Radiation dose
The mean and standard deviation (SD) of the radiation dose indices for both groups are shown in

Qualitative IQ
The qualitative IQ scores of both readers in the MP and AP groups are shown in Table 4. IN, IS, and OIQ showed a statistically significant difference (p < 0.05) between the two groups, with higher scores in the AP group than in the MP group for both readers (Figure 4).There was no significant difference in IA scores (p = 0.652) between the MP and AP group.However, none of the images were rated as suboptimal or unacceptable (score < 4) by the two readers.IN (MP, k = 0.98; AP, k = 0.88), IA (MP and AP, k = 1), IS (MP, k = 0.97; AP, k = 0.92), and OIQ (MP, k = 0.97; AP, k = 0.94) showed almost perfect inter-observer agreement between the two readers.

Discussion
In this study, we evaluated the positioning accuracy, IQ, and RD of artificial intelligence (AI)-based automatic and manual positioning for CT Kidney Ureter Bladder (KUB) imaging.A closer look at off-center distances showed that the off-center positions were significantly lower in the AI-based AP group than in the MP group.The mean off-center distances for the AP and MP groups were 9.662AE6.36mm. and 15.117AE9.55mm.Similar findings were observed in a study by Yadong et al., in which the off-center distance was significantly higher in the MP group (4.05 AE 2.40 cm) than in the AP group (1.56 AE 0.83 cm) for CT thorax imaging. 14The study was performed by Ronald et al. on pediatric patients with and without immobilization devices.They found that utilizing the 3D camera for positioning pediatric patients, without an additional immobilization device, resulted in more precise positioning compared to manual employed by radiographers, which is similar to the findings of adults.Notably, there was no difference in the positioning accuracy between the 3D camera and radiographers for patients placed with an immobilization device. 15Saltybaeva et al. evaluated the accuracy of the 3D camera algorithm for AP and compared the results with those of MP for both chest and abdominal CT.For chest CT, the average difference in off-center was 7 AE 4 mm when using AP and 19 AE 9 mm when the table height was selected manually by technologists.For the abdomen, the average vertical off-centering was 4 AE 2 mm and 18 AE 11 mm for the automatic and MP respectively. 18C techniques are perhaps the most important innovations in terms of dose reduction.3][24] Off-center anatomy can result in suboptimal exposure settings, which affect IQ and increase RD. 25,26 14,27 Our study had some limitations.First, we did not measure and compare patients positioning time in automatic and manual positions.Second, we could not assess whether the off-center distance observed in the study during manual patient positioning was below or above the gantry isocenter.Third, we enrolled patients with a normal BMI in this study.However, we did not specifically address how variations in patient weight may affect the accuracy of patient positioning.

Conclusion
AI-based patient positioning is a touchless system that is operated by a single switch.The AI-based automatic positioning technique aligns the patient to the isocenter of the gantry with less off-center alignment and increases positioning accuracy.Hence, the study concludes that AI-based automatic positioning improves the overall image quality with noise reduction and reduced RD in patients undergoing CT KUB imaging.Further research in this area will improve the role of AI in healthcare optimization and patient care.

Open Peer Review
Current Peer Review Status:

Mustapha Barde
Bayero University, Kano, Kano, Nigeria The article presented a well sound approach in evaluating artificial intelligence (AI)-based method in comparison with manual patient positioning for CT KUB to ascertain the accuracy, radiation dose and image quality of the said methods. it was established that the AI based (automated position) is more robust Than the manual positioning, and it is recommended to be employed in clinical practice to optimize dose.The below minor corrections will be vital please.

Minor correction
The study duration (period within which it was conducted should be stated 1. It will be good to state that, manual approach was employed in off center distance measurement.Also, the lines indicating the off-center distance measurement (figure 1) should be labelled eg ( p,q, r…etc) to aid comprehension 2.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions drawn adequately supported by the results?Tasleem Shaikh Urgent Care, MedStar Health, Alexandria, Virginia, USA The revised version is approved.

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Medical Imaging Technology.

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

Major comments:
The article highlights the advantages of AI-based automatic positioning using precise images in terms of positioning accuracy, radiation dose, and image quality compared to manual positioning for CT KUB examination.The introduction highlights the importance of reducing radiation dose in CT examinations using AI-based automatic positioning, which improves positioning accuracy and image quality with adherence to the ALARA principle.The methodology is well explained.Inclusion criteria for patient selection, measurement of radiation dose parameters, image quality (quantitative and qualitative) parameters, off-center distance, and standardized protocols for both AI-based and manual positioning enhance the study's reproducibility.Appropriate statistical methods were used to analyze the data.The results section presents the findings clearly and organized, using appropriate tables and figures to illustrate the data.The significant improvements in positioning accuracy and reduction in radiation dose with AI-based positioning are well-documented.The 44% reduction in off-center distance suggests a substantial improvement in patient positioning accuracy, which is essential for optimal imaging.The statistically significant reductions in CTDIvol (8.38%), DLP (12.32%),SSDE (10.32%), and ED (12.42%) for the AP group compared to the MP group are noted.Higher qualitative IQ scores support the benefits of AI-based positioning in producing better diagnostic image quality.The almost perfect interobserver agreement suggests that these results are reliable and reproducible.The discussion effectively summarizes the results and implications of improved positioning accuracy and reduced radiation dose for clinical practice.AI's potential to enhance consistency and efficiency in CT KUB scans is well-articulated, and the limitations of the study are welladdressed.The conclusion highlights the potential of AI-based automatic positioning to improve clinical outcomes and reduce radiation exposure for CT KUB examinations.

Minor comments:
Ensure all the abbreviations are clearly defined when first used in the manuscript.Include a section on future research directions of the study.Suggest how the findings of this study could be applied or expanded in future research.Check for typographic errors in the units.The study is well structured, with clear comparisons and statistically significant results that highlight the potential of AI in CT imaging procedures, leading to better patient outcomes and improved workflow in radiology.

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Medical Imaging Technology.

Chandrasekhar Priyanka
Apollo University, Chittoor, Andhra Pradesh, India The article is interesting and innovative in exploring the advantages of AI-based positioning camera which detects the patients contour and allows for automatic positioning in CT KUB examination.

Major comments
The article highlights the AI-based positioning ability of accurately positioning the patient with respect to the isocenter of gantry.It explains the benefits of reduced radiation dose by positioning the patient to isocenter in CT.The improvement in image quality with AI-based deep learning reconstruction algorithm (DLR) with automatic positioning such as precise image compared to iterative reconstruction (iDose 4 ) with manual positioning for CT KUB examinations was presented well in the article using qualitative and quantitative image quality analysis.Radiation is an important concern while performing CT scans.The present study also highlights the 12% reduction in radiation dose which is a substantial improvement, contributing to safer imaging practices.

Minor comments:
A small typographic error was noted in the units of off-center distance between abstract (mm) and results section (cm) which can be corrected.The study focused on CT KUB, requires exploration in other body parts for comprehensive understanding and realizing the full potential of AI-based positioning techniques in CT.
Overall, I feel the article is excellent in the current era of utilizing the advantages of artificial intelligence-based technologies for optimizing the workflow, improving the patient care and comfort in CT.

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
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Figure 2 .
Figure 2.For quantitative assessment of image quality, A shows ROI were placed in upper (ROI 1) and lower pole (ROI 2) of right kidney, upper (ROI 3) and lower pole (ROI 4) of left kidney subcutaneous fat (ROI 5).B shows ROI placed in psoas muscle (ROI 6).

Figure 3 .
Figure 3. Qualitative analysis of image quality.

Figure 4 .
Figure 4. Axial CT KUB image acquired using AI-based automatic positioning technique (A).Axial CT KUB Image acquired using manual positioning technique (B).

: 1 Reviewer
Computed Tomography,Magnetic Resonance Imaging, Radiation dose, Mammography and Fetal MRI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.Version Report 01 July 2024 https://doi.org/10.5256/f1000research.165377.r296053Page 13 of 17 F1000Research 2024, 13:683 Last updated: 09 JUL 2024 © 2024 Shaikh T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reviewer Report 01
July 2024 https://doi.org/10.5256/f1000research.165377.r296056© 2024 Priyanka C.This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 2
. There was a statistically significant difference in the measured CTDIvol (p < 0.05), DLP (p < 0.05), SSDE (p < 0.05), and effective dose (p < 0.05) between the MP and AP groups.The AI based AP group showed 8.38%, 12.32%, 10.32%, 12.42% reductions in CTDIvol, DLP, SSDE, and ED, respectively, compared with MP group.Quantitative IQThe mean and SD of the quantitative IQ parameters for both groups are shown in Table3.Quantitative IQ parameters, such as attenuation of the right kidney (p = 0.740), left kidney (p = 0.570), psoas muscle (p = 0.157), and subcutaneous fat (p = 0.053), did not show significant differences between the MP and AP group.However, other parameters such as IN and SNR of the right kidney, left kidney, psoas muscle, and subcutaneous fat showed statistically significant differences (p < 0.05), with lower IN and higher SNR in the AP group than in the MP group.The AP group showed 46.42% total IN reduction compared to MP group Similarly, the CNR of right and left kidney was higher in AP group compared MP group with significant difference (p < 0.05).

Table 1 .
Summary of patient details.
MP Manual Positioning, AP Automatic Positioning, SD Standard Deviation, BMI Body Mass Index.

Table 2 .
Comparison of radiation dose indices between MP group and AP group.

Table 3 .
Quantitative IQ analysis between MP and AP group.
MP Manual Positioning, AP Automatic Positioning, SD Standard Deviation, HU Hounsfield Unit.

Table 4 .
Qualitative IQ analysis between Manual and Automatic positioning group.
13ne et al.showed 23.8%, 22.8%, 17.2 %, and 20.5 % reductions in radiation dose for CT chest without contrast, abdominal pelvis enterography, chest with contrast, and abdomen pelvis contrast studies, respectively, for 3D camerabased positioning.13Similarfindings were noted in a study by Aly et al., who showed a higher off-center distance in the MP group than in the AP group.Due to the higher off-center distance, the radiation dose parameters such as CTDIv In our study, there were notable reductions in radiation dose metrics such as CTDIv (8.38%), DLP (12.32%),ED (12.42%), and SSDE (10.32%) in AI-based AP group compared to MP group.Yadong et al. observed 16% dose reduction for AP in CT thorax examinations compared to MP. 14 in the AP group than in the MP group.Similar findings were reported by Yadong et al., in which the IN was lower in the AP group than in the MP group.
This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.